Network camera systems, for example network surveillance camera systems or IP camera systems, have existed for a number of years but have undergone relatively slow industry adoption. Compared to traditional analog camera systems, network camera systems offer advantages such as accessibility, integration, low installation costs, scalability, and an ability to move to higher resolution video. Data produced by network cameras, however, demand large amounts of bandwidth and storage capacity.
Bandwidth problems associated with network camera systems have lead to more complex camera networks that include an increased number of switches and, in some cases, complete alternative data paths. Storage problems associated with network camera systems become magnified as video resolution and the number of cameras in a system increase. For example, a single standard D1 resolution camera using MPEG-4 compression and operating at 30 frames-per-second (fps) can require 360 gigabytes (GB) of storage for video data representing one month of video data. A camera system with 1000 cameras, therefore, would require 360 terabytes (TB) of storage for data spanning one month. This example demonstrates a huge cost and facility management challenge presented with network camera systems, especially where mega-pixel resolution is desired and where applications require six months or a year of video data storage. Due to the problems identified, most network video data are not recorded at full quality, but are recorded at lower resolutions and frame rates. Because typical high resolution cameras generate video data requiring a large amount of storage resources within a short period of time, it is impractical for a typical camera to include a self-contained storage unit, such as a hard drive, that is able to store a significant amount of video data.
Typical storage architecture of network camera systems is configured similarly to traditional analog systems. The architecture includes centrally located digital video recorders (DVRs) or network video recorders (NVRs) connected through a network to IP cameras. The typical architecture is inadequate for a number of reasons. For example, most DVRs and NVRs do not include open platforms such that a system is limited to one brand for future replacements and upgrades. Also, most DVRs and NVRs do not meet IT standards for system resiliency, redundancy, and long-term archiving of video data. Additionally, typical network camera systems often lack storage scalability such that, as network camera systems expand, storage systems require “forklift” upgrades.
Another problem with typical video data storage configurations is that many applications require storage devices to continuously run. Such continuous operation causes the storage devices to fail after five or six years of operation. Unless archived or stored redundantly, data on failed storage devices become lost. The need to replace storage devices, therefore, becomes a significant concern and maintenance issue.
Recently, some network camera systems have implemented video analytics processing to identify when important events (such as object movement) are being captured by a video camera. Video analytics has been primarily used to alert security of potential unwanted events. Most video analytics is performed by a central processor that is common to multiple cameras, but some video cameras have built-in video analytics capabilities. These video cameras with built-in analytics, however, have not included large capacity storage due to the large storage requirements of the video data generated by the camera. Also, there are some cameras configured without built-in video analytics but with built-in small storage capacity that is insufficient to serve as a substitute for traditional DVRs and NVRs.
Therefore, a need exists for a network camera system that produces high quality video data, requires less storage capacity and network bandwidth, meets IT standards, is easily scalable, and operates for a longer period of time without storage device replacement.